00cda8cc70
* data loading refactor (wip) * updates * progress * pytest * pytest fix * lint * zero_first -> filelock, more simplifications * small simplification * import change * nit * lint * simplify dedup * couldnt resist * review comments WIP * continued wip * minor changes * fix; remove contrived test * further refactor * set default seed in pydantic config * lint * continued simplication * lint * renaming and nits * filelock tests * fix * fix * lint * remove nullable arg * remove unnecessary code * moving dataset save fn to shared module * remove debug print * matching var naming * fn name change * coderabbit comments * naming nit * fix test
110 lines
3.7 KiB
Python
110 lines
3.7 KiB
Python
"""
|
|
E2E smoke tests for LLMCompressorPlugin integration
|
|
"""
|
|
|
|
from pathlib import Path
|
|
|
|
import pytest
|
|
|
|
from axolotl.common.datasets import load_datasets
|
|
from axolotl.train import train
|
|
from axolotl.utils.config import normalize_config, prepare_plugins, validate_config
|
|
from axolotl.utils.dict import DictDefault
|
|
|
|
from tests.e2e.utils import (
|
|
check_model_output_exists,
|
|
require_llmcompressor,
|
|
require_torch_2_4_1,
|
|
)
|
|
|
|
MODELS = [
|
|
"nm-testing/llama2.c-stories42M-pruned2.4-compressed",
|
|
"nm-testing/llama2.c-stories42M-gsm8k-sparse-only-compressed",
|
|
]
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"base_model", MODELS, ids=["no-checkpoint-recipe", "with-checkpoint-recipe"]
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"save_compressed", [True, False], ids=["save_compressed", "save_uncompressed"]
|
|
)
|
|
class TestLLMCompressorIntegration:
|
|
"""
|
|
e2e tests for axolotl.integrations.llm_compressor.LLMCompressorPlugin
|
|
"""
|
|
|
|
@require_llmcompressor
|
|
@require_torch_2_4_1
|
|
def test_llmcompressor_plugin(
|
|
self, temp_dir, base_model: str, save_compressed: bool
|
|
):
|
|
from llmcompressor import active_session
|
|
|
|
# core cfg
|
|
cfg = DictDefault(
|
|
{
|
|
"base_model": base_model,
|
|
"plugins": ["axolotl.integrations.llm_compressor.LLMCompressorPlugin"],
|
|
"sequence_len": 1024,
|
|
"val_set_size": 0.05,
|
|
"special_tokens": {"pad_token": "<|endoftext|>"},
|
|
"datasets": [{"path": "mhenrichsen/alpaca_2k_test", "type": "alpaca"}],
|
|
"num_epochs": 1,
|
|
"micro_batch_size": 2,
|
|
"gradient_accumulation_steps": 2,
|
|
"output_dir": temp_dir,
|
|
"learning_rate": 1e-5,
|
|
"optimizer": "adamw_torch_fused",
|
|
"lr_scheduler": "cosine",
|
|
"save_safetensors": True,
|
|
"bf16": "auto",
|
|
"max_steps": 5,
|
|
"llmcompressor": {
|
|
"recipe": {
|
|
"finetuning_stage": {
|
|
"finetuning_modifiers": {
|
|
"ConstantPruningModifier": {
|
|
"targets": [
|
|
"re:.*q_proj.weight",
|
|
"re:.*k_proj.weight",
|
|
"re:.*v_proj.weight",
|
|
"re:.*o_proj.weight",
|
|
"re:.*gate_proj.weight",
|
|
"re:.*up_proj.weight",
|
|
"re:.*down_proj.weight",
|
|
],
|
|
"start": 0,
|
|
},
|
|
},
|
|
},
|
|
},
|
|
"save_compressed": save_compressed,
|
|
},
|
|
}
|
|
)
|
|
|
|
prepare_plugins(cfg)
|
|
cfg = validate_config(cfg)
|
|
normalize_config(cfg)
|
|
dataset_meta = load_datasets(cfg=cfg)
|
|
|
|
try:
|
|
train(cfg=cfg, dataset_meta=dataset_meta)
|
|
check_model_output_exists(temp_dir, cfg)
|
|
_check_llmcompressor_model_outputs(temp_dir, save_compressed)
|
|
finally:
|
|
active_session().reset()
|
|
|
|
|
|
def _check_llmcompressor_model_outputs(temp_dir, save_compressed):
|
|
if save_compressed:
|
|
assert (Path(temp_dir) / "recipe.yaml").exists()
|
|
|
|
from compressed_tensors import ModelCompressor
|
|
from compressed_tensors.config import Sparse24BitMaskConfig
|
|
|
|
compressor = ModelCompressor.from_pretrained(temp_dir)
|
|
assert compressor is not None
|
|
assert isinstance(compressor.sparsity_config, Sparse24BitMaskConfig)
|